INTERDISCIPLINARY RESEARCH ON MUSEUM SERVICE DESIGN AND DEVELOPMENT OF DESIGN INFORMATICS ANALYSIS SYSTEM

Since its inception, design has possessed an inherently interdisciplinary nature. Its core user-centric concept requires the integration of knowledge from sociology, psychology, engineering, and other fields to address complex real-world problems. However, compared with the natural sciences and engineering, design research has long been dominated by qualitative analysis. The lack of quantitative methods often causes design conclusions to rely on empirical judgment, making it difficult to establish verifiable and reproducible scientific paradigms. This limitation also constrains its academic influence and social impact in interdisciplinary collaboration. At present, the rapid development of data science provides essential technological support for the quantitative transformation of design. Quantitative analysis can not only deconstruct the internal logic of design phenomena and construct computable theoretical models, but also enhance the scientific rigor and methodological robustness of design research. However, design researchers generally face two core dilemmas: first, the lack of quantitative analysis tools tailored to design research scenarios, as traditional statistical software is complex to operate and lacks modules specific to design; second, the difficulty of effectively integrating quantitative methods with design problems, since existing applications are mostly confined to single fields and lack interdisciplinary methodological guidance. This thesis focuses on museum service design. First, it examines the national policies and agendas of China and Italy, clarifying the strategic positioning of museum service design in cultural heritage preservation, digital transformation, and public services. Through a literature review, it identifies the limitations of quantitative analysis, interdisciplinary integration, and data-driven methods in museum service design. Second, it constructs a quantitative research framework that integrates design, data science, and museology, including knowledge graphs and interdisciplinary research methods. Third, this thesis combines qualitative research (such as case studies and interviews) with quantitative methods (such as multiple linear regression and machine learning), using museum cultural heritage data as a sample to build a potential visitor classification model and determine the optimal proportions of museum functional spaces. Two case studies illustrate in detail how quantitative analysis can be applied to research on museum service design. Finally, this thesis develops a computer-aided design analysis system that integrates data cleaning, statistical analysis, and result visualization functions, and is accompanied by a user manual. This provides design researchers with a specialized statistical analysis tool aimed at filling the gap in theoretical support for museum service optimization and the absence of quantitative analysis tools in design research. In an era where digital and intelligent technologies are profoundly reshaping the boundaries of disciplines, the quantitative and interdisciplinary transformation of design is not only an intrinsic requirement for overcoming academic bottlenecks and consolidating the foundations of disciplines, but also an inevitable response to national cultural strategies and the improvement of the quality of public cultural services. This exploration bridges innovation in design and cultural heritage, enabling the humanistic value of design to be effectively conveyed to the public through scientific methods and establishing a practical paradigm for interdisciplinary innovation in culture, technology, and other fields within the discipline of design.